It's possible to do transformative, useful things with data

With all this data, something amazing is happening. It's now
possible to creatively apply scientific principles to design tools
and processes in areas where scientific approaches have been
infeasible due to the difficulty or expense of collecting data. We
think this is just the beginning. We're in the midst of a Cambrian
explosion of problems where a scientific approach, until now, has
been too difficult, too costly, or too complex. Data and
quantitative methods have the power to improve our businesses, NGOs,
governments, and society as a whole.

It's really hard to identify and do these things.

There are two huge jumps to make on any data project: the gap
between having a problem and knowing how you could address it with
data, and the gap between knowing what you want to do and knowing
how to do it the right way. Most data projects whiff on either the
first one or the second.

Datascope was founded in 2009 to address this.

There's a right way to do it.

We're certainly not the first people to have these realizations, but we think
our approach sets us apart.

Tailored beats one-size-fits-all.

If you've got a data science opportunity that fits the description above,
off-the-shelf solutions probably don't exist yet. Depending on your problem
and how common it is, they may never exist. We believe that the way to improve
people, organizations, and even society is by tailoring an approach to the
specific needs, culture, and goals of the problem at hand.

A team with diverse thoughts and backgrounds beats a homogeneous
one.

A data science team must be able to imagine a wide array of potential
solutions and continually make decisions about which ones to pursue and which
ones to throw out. That's why, for us, building a heterogeneous team is as
much a pragmatic decision as anything else; research
has shown that diverse teams consistently outperform homogeneous ones at
creative tasks and decision making. Having a team with many backgrounds allows
us to understand a broader set of client problems and come up with better
solutions to them.

Skeptical beats dogmatic

Smart people tend to think they know a lot, rightfully so. Smarter people
continually re-evaluate what they know, and keep their ears open to what
others are saying, whether those people are their colleagues, competitors, or
clients.

Concise and user-centered beats jargon-filled and haphazard

Data science results can be complex and nuanced, but it should not require
data science skill to understand them. We believe it is always the
responsibility of the data scientist to communicate results and design tools
in ways that are meaningful to others.

Open-source beats proprietary

Open source is a proven way of collaborating to create the best software. It
gives everyone the freedom to see the code, learn from it, ask questions, and
build on it. Science and engineering, like open source, thrive in an open
environment where people share ideas and build on the work of others. Novel
ideas come from connecting and remixing ideas that came before. Open source
makes it happen faster and more reliably.

Mike beats Dean

This is just a generally accepted fact.

We can't do it alone.

We believe the world will be a better place with more data scientists and
better trained ones, whether they work for us or somewhere else. Datascope
takes active steps towards helping the data science community grow by: